Overview

Brought to you by YData

Dataset statistics

Number of variables40
Number of observations19041
Missing cells334368
Missing cells (%)43.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory25.9 MiB
Average record size in memory1.4 KiB

Variable types

Text8
Categorical8
Numeric2
Unsupported17
DateTime5

Alerts

Modalidad de atencion has constant value "Presencial" Constant
Carpeta is highly overall correlated with Fila and 2 other fieldsHigh correlation
Codigo de Oficina is highly overall correlated with OficinaHigh correlation
Fila is highly overall correlated with Carpeta and 2 other fieldsHigh correlation
Oficina is highly overall correlated with Codigo de OficinaHigh correlation
Perdido is highly overall correlated with SaltadoHigh correlation
Prefijo Ticket is highly overall correlated with Carpeta and 2 other fieldsHigh correlation
Saltado is highly overall correlated with PerdidoHigh correlation
Segmento is highly overall correlated with Carpeta and 2 other fieldsHigh correlation
Perdido is highly imbalanced (98.6%) Imbalance
Saltado is highly imbalanced (98.6%) Imbalance
DNI Ejecutivo DV has 19041 (100.0%) missing values Missing
DNI Cliente DV has 19041 (100.0%) missing values Missing
Email Cliente has 18793 (98.7%) missing values Missing
Rating has 19041 (100.0%) missing values Missing
Comentario has 19041 (100.0%) missing values Missing
Nombre Cliente has 4715 (24.8%) missing values Missing
Formulario has 19041 (100.0%) missing values Missing
Segmento has 4965 (26.1%) missing values Missing
ID de llamada anterior has 19041 (100.0%) missing values Missing
UTM has 19041 (100.0%) missing values Missing
ID Submotivos has 19041 (100.0%) missing values Missing
Submotivos de atencion has 19041 (100.0%) missing values Missing
Carpeta has 1233 (6.5%) missing values Missing
Categorizacion-Derivacion has 19041 (100.0%) missing values Missing
Tipo de cliente has 19041 (100.0%) missing values Missing
Pregunta Botonera has 19041 (100.0%) missing values Missing
Carpeta de motivos has 19041 (100.0%) missing values Missing
Preferencia Usuario has 19041 (100.0%) missing values Missing
Submotivos v2 has 19041 (100.0%) missing values Missing
Sucursal de origen has 19041 (100.0%) missing values Missing
ID Ticket has unique values Unique
ID Llamada has unique values Unique
Modulo is an unsupported type, check if it needs cleaning or further analysis Unsupported
DNI Ejecutivo DV is an unsupported type, check if it needs cleaning or further analysis Unsupported
DNI Cliente DV is an unsupported type, check if it needs cleaning or further analysis Unsupported
Rating is an unsupported type, check if it needs cleaning or further analysis Unsupported
Comentario is an unsupported type, check if it needs cleaning or further analysis Unsupported
Formulario is an unsupported type, check if it needs cleaning or further analysis Unsupported
ID de llamada anterior is an unsupported type, check if it needs cleaning or further analysis Unsupported
UTM is an unsupported type, check if it needs cleaning or further analysis Unsupported
ID Submotivos is an unsupported type, check if it needs cleaning or further analysis Unsupported
Submotivos de atencion is an unsupported type, check if it needs cleaning or further analysis Unsupported
Categorizacion-Derivacion is an unsupported type, check if it needs cleaning or further analysis Unsupported
Tipo de cliente is an unsupported type, check if it needs cleaning or further analysis Unsupported
Pregunta Botonera is an unsupported type, check if it needs cleaning or further analysis Unsupported
Carpeta de motivos is an unsupported type, check if it needs cleaning or further analysis Unsupported
Preferencia Usuario is an unsupported type, check if it needs cleaning or further analysis Unsupported
Submotivos v2 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Sucursal de origen is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2024-11-29 18:08:39.022559
Analysis finished2024-11-29 18:08:46.815802
Duration7.79 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

ID Ticket
Text

Unique 

Distinct19041
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2024-11-29T15:08:46.975123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length14.160653
Min length14

Characters and Unicode

Total characters269633
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19041 ?
Unique (%)100.0%

Sample

1st rowT3270200138752
2nd rowT3270200274435
3rd rowT3270200199076
4th rowT3270200210373
5th rowT3270200318250
ValueCountFrequency (%)
t3270200138752 1
 
< 0.1%
t3270200199076 1
 
< 0.1%
t3270200318250 1
 
< 0.1%
t3270200464273 1
 
< 0.1%
t3270200587221 1
 
< 0.1%
t3270200643964 1
 
< 0.1%
t3270200784328 1
 
< 0.1%
t3270200857982 1
 
< 0.1%
t3270200124178 1
 
< 0.1%
t3270200201637 1
 
< 0.1%
Other values (19031) 19031
99.9%
2024-11-29T15:08:47.299146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 47715
17.7%
3 43557
16.2%
1 30549
11.3%
0 29146
10.8%
8 20361
7.6%
T 19041
 
7.1%
7 18042
 
6.7%
9 17360
 
6.4%
4 15676
 
5.8%
5 14778
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 269633
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 47715
17.7%
3 43557
16.2%
1 30549
11.3%
0 29146
10.8%
8 20361
7.6%
T 19041
 
7.1%
7 18042
 
6.7%
9 17360
 
6.4%
4 15676
 
5.8%
5 14778
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 269633
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 47715
17.7%
3 43557
16.2%
1 30549
11.3%
0 29146
10.8%
8 20361
7.6%
T 19041
 
7.1%
7 18042
 
6.7%
9 17360
 
6.4%
4 15676
 
5.8%
5 14778
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 269633
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 47715
17.7%
3 43557
16.2%
1 30549
11.3%
0 29146
10.8%
8 20361
7.6%
T 19041
 
7.1%
7 18042
 
6.7%
9 17360
 
6.4%
4 15676
 
5.8%
5 14778
 
5.5%

ID Llamada
Text

Unique 

Distinct19041
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
2024-11-29T15:08:47.500090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length40
Median length38
Mean length38.160653
Min length38

Characters and Unicode

Total characters726617
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19041 ?
Unique (%)100.0%

Sample

1st rowC2509732198327020013875217307240683370
2nd rowC2509732198327020027443517307334890300
3rd rowC2509732202327020019907617307247753890
4th rowC2509732202327020021037317307250432900
5th rowC2555532202327020031825017307254844290
ValueCountFrequency (%)
c2509732198327020013875217307240683370 1
 
< 0.1%
c2509732202327020019907617307247753890 1
 
< 0.1%
c2555532202327020031825017307254844290 1
 
< 0.1%
c2555532202327020046427317307268761580 1
 
< 0.1%
c2555532202327020058722117307292716520 1
 
< 0.1%
c2555532202327020064396417307302644760 1
 
< 0.1%
c2545732202327020078432817307368992670 1
 
< 0.1%
c2555532202327020085798217307420757140 1
 
< 0.1%
c2555532201327020012417817307352522110 1
 
< 0.1%
c2555532201327020020163717307372192060 1
 
< 0.1%
Other values (19031) 19031
99.9%
2024-11-29T15:08:47.834177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 116971
16.1%
3 112403
15.5%
1 99310
13.7%
0 83299
11.5%
5 65130
9.0%
7 59986
8.3%
8 48719
6.7%
9 43529
 
6.0%
4 43344
 
6.0%
6 34885
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 726617
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 116971
16.1%
3 112403
15.5%
1 99310
13.7%
0 83299
11.5%
5 65130
9.0%
7 59986
8.3%
8 48719
6.7%
9 43529
 
6.0%
4 43344
 
6.0%
6 34885
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 726617
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 116971
16.1%
3 112403
15.5%
1 99310
13.7%
0 83299
11.5%
5 65130
9.0%
7 59986
8.3%
8 48719
6.7%
9 43529
 
6.0%
4 43344
 
6.0%
6 34885
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 726617
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 116971
16.1%
3 112403
15.5%
1 99310
13.7%
0 83299
11.5%
5 65130
9.0%
7 59986
8.3%
8 48719
6.7%
9 43529
 
6.0%
4 43344
 
6.0%
6 34885
 
4.8%

Oficina
Categorical

High correlation 

Distinct37
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
AFP PlanVital Tenderini
3284 
AFP PlanVital Concepcion
 
1021
AFP PlanVital Temuco
 
890
AFP PlanVital Apoquindo
 
821
AFP PlanVital Talca
 
768
Other values (32)
12257 

Length

Max length28
Median length25
Mean length22.484271
Min length19

Characters and Unicode

Total characters428123
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAFP PlanVital Antofagasta
2nd rowAFP PlanVital Antofagasta
3rd rowAFP PlanVital Antofagasta
4th rowAFP PlanVital Antofagasta
5th rowAFP PlanVital Antofagasta

Common Values

ValueCountFrequency (%)
AFP PlanVital Tenderini 3284
 
17.2%
AFP PlanVital Concepcion 1021
 
5.4%
AFP PlanVital Temuco 890
 
4.7%
AFP PlanVital Apoquindo 821
 
4.3%
AFP PlanVital Talca 768
 
4.0%
AFP PlanVital La Serena 690
 
3.6%
AFP PlanVital Rancagua 678
 
3.6%
AFP PlanVital Maipu 626
 
3.3%
AFP PlanVital San Fernando 609
 
3.2%
AFP PlanVital Vina del Mar 600
 
3.2%
Other values (27) 9054
47.6%

Length

2024-11-29T15:08:47.965935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
afp 19041
30.5%
planvital 19041
30.5%
tenderini 3284
 
5.3%
san 1663
 
2.7%
concepcion 1021
 
1.6%
temuco 890
 
1.4%
apoquindo 821
 
1.3%
talca 768
 
1.2%
la 690
 
1.1%
serena 690
 
1.1%
Other values (37) 14502
23.2%

Most occurring characters

ValueCountFrequency (%)
a 55345
12.9%
l 43528
10.2%
43370
10.1%
n 39065
9.1%
P 38987
9.1%
i 34209
 
8.0%
t 23535
 
5.5%
A 21863
 
5.1%
V 20803
 
4.9%
F 19867
 
4.6%
Other values (25) 87551
20.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 428123
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 55345
12.9%
l 43528
10.2%
43370
10.1%
n 39065
9.1%
P 38987
9.1%
i 34209
 
8.0%
t 23535
 
5.5%
A 21863
 
5.1%
V 20803
 
4.9%
F 19867
 
4.6%
Other values (25) 87551
20.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 428123
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 55345
12.9%
l 43528
10.2%
43370
10.1%
n 39065
9.1%
P 38987
9.1%
i 34209
 
8.0%
t 23535
 
5.5%
A 21863
 
5.1%
V 20803
 
4.9%
F 19867
 
4.6%
Other values (25) 87551
20.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 428123
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 55345
12.9%
l 43528
10.2%
43370
10.1%
n 39065
9.1%
P 38987
9.1%
i 34209
 
8.0%
t 23535
 
5.5%
A 21863
 
5.1%
V 20803
 
4.9%
F 19867
 
4.6%
Other values (25) 87551
20.4%

Codigo de Oficina
Real number (ℝ)

High correlation 

Distinct37
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.771388
Minimum20
Maximum186
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.9 KiB
2024-11-29T15:08:48.066241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile22
Q147
median61
Q384
95-th percentile124
Maximum186
Range166
Interquartile range (IQR)37

Descriptive statistics

Standard deviation30.254602
Coefficient of variation (CV)0.45999641
Kurtosis3.2490523
Mean65.771388
Median Absolute Deviation (MAD)18
Skewness1.2988819
Sum1252353
Variance915.34096
MonotonicityNot monotonic
2024-11-29T15:08:48.174710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
50 3284
 
17.2%
80 1021
 
5.4%
91 890
 
4.7%
124 821
 
4.3%
72 768
 
4.0%
41 690
 
3.6%
61 678
 
3.6%
56 626
 
3.3%
62 609
 
3.2%
47 600
 
3.2%
Other values (27) 9054
47.6%
ValueCountFrequency (%)
20 405
2.1%
21 295
1.5%
22 465
2.4%
23 403
2.1%
24 323
1.7%
31 217
 
1.1%
40 367
1.9%
41 690
3.6%
43 366
1.9%
44 164
 
0.9%
ValueCountFrequency (%)
186 383
2.0%
124 821
4.3%
98 119
 
0.6%
97 177
 
0.9%
96 200
 
1.1%
95 233
 
1.2%
94 420
2.2%
93 587
3.1%
92 371
1.9%
91 890
4.7%

Fila
Categorical

High correlation 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
Certificados
5521 
Otros Pensionado
3453 
Otros
3352 
Solicitud tramite de pension
2396 
Liquidacion de pension
1299 
Other values (8)
3020 

Length

Max length28
Median length19
Mean length14.761777
Min length5

Characters and Unicode

Total characters281079
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowActualizacion de datos
2nd rowActualizacion de datos
3rd rowCertificados
4th rowCertificados
5th rowCertificados

Common Values

ValueCountFrequency (%)
Certificados 5521
29.0%
Otros Pensionado 3453
18.1%
Otros 3352
17.6%
Solicitud tramite de pension 2396
12.6%
Liquidacion de pension 1299
 
6.8%
Actualizacion de datos 896
 
4.7%
Empleador 787
 
4.1%
Giro APV o Cuenta 2 692
 
3.6%
Traspaso 241
 
1.3%
Pago en exceso 154
 
0.8%
Other values (3) 250
 
1.3%

Length

2024-11-29T15:08:48.294024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
otros 6805
18.2%
certificados 5521
14.8%
de 4633
12.4%
pension 3838
10.3%
pensionado 3453
9.2%
solicitud 2396
 
6.4%
tramite 2396
 
6.4%
liquidacion 1299
 
3.5%
actualizacion 896
 
2.4%
datos 896
 
2.4%
Other values (14) 5242
14.0%

Most occurring characters

ValueCountFrequency (%)
i 32074
11.4%
o 31403
11.2%
t 22063
 
7.8%
e 21847
 
7.8%
s 21214
 
7.5%
d 19027
 
6.8%
18334
 
6.5%
n 17730
 
6.3%
a 17644
 
6.3%
r 16507
 
5.9%
Other values (23) 63236
22.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 281079
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 32074
11.4%
o 31403
11.2%
t 22063
 
7.8%
e 21847
 
7.8%
s 21214
 
7.5%
d 19027
 
6.8%
18334
 
6.5%
n 17730
 
6.3%
a 17644
 
6.3%
r 16507
 
5.9%
Other values (23) 63236
22.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 281079
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 32074
11.4%
o 31403
11.2%
t 22063
 
7.8%
e 21847
 
7.8%
s 21214
 
7.5%
d 19027
 
6.8%
18334
 
6.5%
n 17730
 
6.3%
a 17644
 
6.3%
r 16507
 
5.9%
Other values (23) 63236
22.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 281079
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 32074
11.4%
o 31403
11.2%
t 22063
 
7.8%
e 21847
 
7.8%
s 21214
 
7.5%
d 19027
 
6.8%
18334
 
6.5%
n 17730
 
6.3%
a 17644
 
6.3%
r 16507
 
5.9%
Other values (23) 63236
22.5%

Modulo
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size706.3 KiB

Fecha
Date

Distinct19
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size148.9 KiB
Minimum2024-11-04 00:00:00
Maximum2024-11-28 00:00:00
2024-11-29T15:08:48.391685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T15:08:48.485879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)

Prefijo Ticket
Categorical

High correlation 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size940.6 KiB
AC
4458 
P
3453 
O
3352 
PT
2396 
PL
1299 
Other values (9)
4083 

Length

Max length2
Median length2
Mean length1.5777008
Min length1

Characters and Unicode

Total characters30041
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAD
2nd rowAD
3rd rowAC
4th rowAC
5th rowAC

Common Values

ValueCountFrequency (%)
AC 4458
23.4%
P 3453
18.1%
O 3352
17.6%
PT 2396
12.6%
PL 1299
 
6.8%
PS 1063
 
5.6%
AD 896
 
4.7%
E 787
 
4.1%
AG 692
 
3.6%
T 241
 
1.3%
Other values (4) 404
 
2.1%

Length

2024-11-29T15:08:48.588093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ac 4458
23.4%
p 3453
18.1%
o 3352
17.6%
pt 2396
12.6%
pl 1299
 
6.8%
ps 1063
 
5.6%
ad 896
 
4.7%
e 787
 
4.1%
ag 692
 
3.6%
t 241
 
1.3%
Other values (4) 404
 
2.1%

Most occurring characters

ValueCountFrequency (%)
P 8365
27.8%
A 6307
21.0%
C 4458
14.8%
O 3352
11.2%
T 2637
 
8.8%
L 1299
 
4.3%
S 1063
 
3.5%
D 896
 
3.0%
E 787
 
2.6%
G 692
 
2.3%
Other values (2) 185
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30041
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 8365
27.8%
A 6307
21.0%
C 4458
14.8%
O 3352
11.2%
T 2637
 
8.8%
L 1299
 
4.3%
S 1063
 
3.5%
D 896
 
3.0%
E 787
 
2.6%
G 692
 
2.3%
Other values (2) 185
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30041
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 8365
27.8%
A 6307
21.0%
C 4458
14.8%
O 3352
11.2%
T 2637
 
8.8%
L 1299
 
4.3%
S 1063
 
3.5%
D 896
 
3.0%
E 787
 
2.6%
G 692
 
2.3%
Other values (2) 185
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30041
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 8365
27.8%
A 6307
21.0%
C 4458
14.8%
O 3352
11.2%
T 2637
 
8.8%
L 1299
 
4.3%
S 1063
 
3.5%
D 896
 
3.0%
E 787
 
2.6%
G 692
 
2.3%
Other values (2) 185
 
0.6%

Nro. Ticket
Real number (ℝ)

Distinct104
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3493514
Minimum1
Maximum104
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size148.9 KiB
2024-11-29T15:08:48.702445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q37
95-th percentile26
Maximum104
Range103
Interquartile range (IQR)6

Descriptive statistics

Standard deviation9.5446983
Coefficient of variation (CV)1.5032556
Kurtosis19.180973
Mean6.3493514
Median Absolute Deviation (MAD)2
Skewness3.8178316
Sum120898
Variance91.101267
MonotonicityNot monotonic
2024-11-29T15:08:48.842334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 4793
25.2%
2 3191
16.8%
3 2239
11.8%
4 1643
 
8.6%
5 1259
 
6.6%
6 984
 
5.2%
7 779
 
4.1%
8 621
 
3.3%
9 478
 
2.5%
10 390
 
2.0%
Other values (94) 2664
14.0%
ValueCountFrequency (%)
1 4793
25.2%
2 3191
16.8%
3 2239
11.8%
4 1643
 
8.6%
5 1259
 
6.6%
6 984
 
5.2%
7 779
 
4.1%
8 621
 
3.3%
9 478
 
2.5%
10 390
 
2.0%
ValueCountFrequency (%)
104 1
< 0.1%
103 1
< 0.1%
102 1
< 0.1%
101 1
< 0.1%
100 1
< 0.1%
99 1
< 0.1%
98 1
< 0.1%
97 1
< 0.1%
96 1
< 0.1%
95 1
< 0.1%
Distinct118
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2024-11-29T15:08:49.151862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9312011
Min length9

Characters and Unicode

Total characters189100
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12612953-K
2nd row12612953-K
3rd row12612953-K
4th row12612953-K
5th row19445974-2
ValueCountFrequency (%)
15253118-4 532
 
2.8%
13773752-3 480
 
2.5%
17051427-0 422
 
2.2%
16386186-0 384
 
2.0%
13896868-5 383
 
2.0%
15603817-2 367
 
1.9%
16542180-9 347
 
1.8%
15989433-9 345
 
1.8%
17769282-4 339
 
1.8%
12296514-7 331
 
1.7%
Other values (108) 15111
79.4%
2024-11-29T15:08:49.521129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 31955
16.9%
- 19041
10.1%
7 18183
9.6%
5 17873
9.5%
3 17035
9.0%
2 16937
9.0%
6 14096
7.5%
8 13647
7.2%
9 13540
7.2%
4 13240
7.0%
Other values (2) 13553
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 189100
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 31955
16.9%
- 19041
10.1%
7 18183
9.6%
5 17873
9.5%
3 17035
9.0%
2 16937
9.0%
6 14096
7.5%
8 13647
7.2%
9 13540
7.2%
4 13240
7.0%
Other values (2) 13553
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 189100
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 31955
16.9%
- 19041
10.1%
7 18183
9.6%
5 17873
9.5%
3 17035
9.0%
2 16937
9.0%
6 14096
7.5%
8 13647
7.2%
9 13540
7.2%
4 13240
7.0%
Other values (2) 13553
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 189100
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 31955
16.9%
- 19041
10.1%
7 18183
9.6%
5 17873
9.5%
3 17035
9.0%
2 16937
9.0%
6 14096
7.5%
8 13647
7.2%
9 13540
7.2%
4 13240
7.0%
Other values (2) 13553
7.2%

DNI Ejecutivo DV
Unsupported

Missing  Rejected  Unsupported 

Missing19041
Missing (%)100.0%
Memory size148.9 KiB
Distinct119
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2024-11-29T15:08:49.811676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length38
Median length31
Mean length25.133659
Min length4

Characters and Unicode

Total characters478570
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHENRIQUEZ HUERTA, VANESSA
2nd rowHENRIQUEZ HUERTA, VANESSA
3rd rowHENRIQUEZ HUERTA, VANESSA
4th rowHENRIQUEZ HUERTA, VANESSA
5th rowMARJORIE NEVENKA PIZARRO OCARANZA
ValueCountFrequency (%)
munoz 1287
 
2.1%
gonzalez 1240
 
2.0%
soto 1210
 
1.9%
del 897
 
1.4%
andrea 776
 
1.2%
torres 725
 
1.2%
fuentes 715
 
1.1%
pino 697
 
1.1%
claudia 648
 
1.0%
diaz 644
 
1.0%
Other values (279) 53679
85.9%
2024-11-29T15:08:50.245379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 71774
15.0%
E 46571
 
9.7%
43477
 
9.1%
R 38846
 
8.1%
N 32610
 
6.8%
O 30819
 
6.4%
I 26965
 
5.6%
L 26277
 
5.5%
S 19794
 
4.1%
, 16523
 
3.5%
Other values (22) 124914
26.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 478570
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 71774
15.0%
E 46571
 
9.7%
43477
 
9.1%
R 38846
 
8.1%
N 32610
 
6.8%
O 30819
 
6.4%
I 26965
 
5.6%
L 26277
 
5.5%
S 19794
 
4.1%
, 16523
 
3.5%
Other values (22) 124914
26.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 478570
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 71774
15.0%
E 46571
 
9.7%
43477
 
9.1%
R 38846
 
8.1%
N 32610
 
6.8%
O 30819
 
6.4%
I 26965
 
5.6%
L 26277
 
5.5%
S 19794
 
4.1%
, 16523
 
3.5%
Other values (22) 124914
26.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 478570
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 71774
15.0%
E 46571
 
9.7%
43477
 
9.1%
R 38846
 
8.1%
N 32610
 
6.8%
O 30819
 
6.4%
I 26965
 
5.6%
L 26277
 
5.5%
S 19794
 
4.1%
, 16523
 
3.5%
Other values (22) 124914
26.1%
Distinct16072
Distinct (%)84.4%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2024-11-29T15:08:50.503567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.5871015
Min length1

Characters and Unicode

Total characters182548
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13809 ?
Unique (%)72.5%

Sample

1st row25310066-4
2nd row25328334-3
3rd row26268695-7
4th row19102988-7
5th row20246505-6
ValueCountFrequency (%)
9725117-7 16
 
0.1%
13257188-0 15
 
0.1%
12872238-6 9
 
< 0.1%
27108951-1 9
 
< 0.1%
10117437-9 8
 
< 0.1%
15185929-1 7
 
< 0.1%
8311919-5 7
 
< 0.1%
9232879-1 7
 
< 0.1%
19112375-1 7
 
< 0.1%
6609835-4 6
 
< 0.1%
Other values (16061) 18947
99.5%
2024-11-29T15:08:50.867064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 21692
11.9%
- 19038
10.4%
2 17769
9.7%
8 16310
8.9%
9 15977
8.8%
7 15672
8.6%
5 15623
8.6%
6 15419
8.4%
0 15005
8.2%
4 14417
7.9%
Other values (3) 15626
8.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 182548
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 21692
11.9%
- 19038
10.4%
2 17769
9.7%
8 16310
8.9%
9 15977
8.8%
7 15672
8.6%
5 15623
8.6%
6 15419
8.4%
0 15005
8.2%
4 14417
7.9%
Other values (3) 15626
8.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 182548
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 21692
11.9%
- 19038
10.4%
2 17769
9.7%
8 16310
8.9%
9 15977
8.8%
7 15672
8.6%
5 15623
8.6%
6 15419
8.4%
0 15005
8.2%
4 14417
7.9%
Other values (3) 15626
8.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 182548
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 21692
11.9%
- 19038
10.4%
2 17769
9.7%
8 16310
8.9%
9 15977
8.8%
7 15672
8.6%
5 15623
8.6%
6 15419
8.4%
0 15005
8.2%
4 14417
7.9%
Other values (3) 15626
8.6%

DNI Cliente DV
Unsupported

Missing  Rejected  Unsupported 

Missing19041
Missing (%)100.0%
Memory size148.9 KiB
Distinct13206
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Memory size148.9 KiB
Minimum2024-11-29 08:40:45
Maximum2024-11-29 17:34:45
2024-11-29T15:08:50.998745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T15:08:51.136232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct13222
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Memory size148.9 KiB
Minimum2024-11-29 08:41:00
Maximum2024-11-29 17:39:34
2024-11-29T15:08:51.262136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T15:08:51.542074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1351
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size148.9 KiB
Minimum2024-11-29 00:00:01
Maximum2024-11-29 01:21:07
2024-11-29T15:08:51.674916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T15:08:51.809185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2702
Distinct (%)14.2%
Missing3
Missing (%)< 0.1%
Memory size148.9 KiB
Minimum2024-11-29 00:00:03
Maximum2024-11-29 04:38:09
2024-11-29T15:08:51.925519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T15:08:52.062988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct148
Distinct (%)0.8%
Missing3
Missing (%)< 0.1%
Memory size1.2 MiB
2024-11-29T15:08:52.272367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length96
Median length92
Mean length17.019908
Min length5

Characters and Unicode

Total characters324025
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)0.3%

Sample

1st rowOtros
2nd rowActualizacion de Datos
3rd rowActualizacion de Datos
4th rowCertificados
5th rowCertificados
ValueCountFrequency (%)
de 6687
17.6%
certificados 5082
13.4%
pension 4369
11.5%
otros 3824
10.1%
tramite 2800
 
7.4%
actualizacion 1643
 
4.3%
certificados,otros 1643
 
4.3%
cai 922
 
2.4%
cav 922
 
2.4%
ccv 907
 
2.4%
Other values (138) 9204
24.2%
2024-11-29T15:08:52.608705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 33373
 
10.3%
e 29740
 
9.2%
o 27839
 
8.6%
t 25810
 
8.0%
s 24486
 
7.6%
r 23392
 
7.2%
a 21909
 
6.8%
18965
 
5.9%
d 17027
 
5.3%
c 15052
 
4.6%
Other values (31) 86432
26.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 324025
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 33373
 
10.3%
e 29740
 
9.2%
o 27839
 
8.6%
t 25810
 
8.0%
s 24486
 
7.6%
r 23392
 
7.2%
a 21909
 
6.8%
18965
 
5.9%
d 17027
 
5.3%
c 15052
 
4.6%
Other values (31) 86432
26.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 324025
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 33373
 
10.3%
e 29740
 
9.2%
o 27839
 
8.6%
t 25810
 
8.0%
s 24486
 
7.6%
r 23392
 
7.2%
a 21909
 
6.8%
18965
 
5.9%
d 17027
 
5.3%
c 15052
 
4.6%
Other values (31) 86432
26.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 324025
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 33373
 
10.3%
e 29740
 
9.2%
o 27839
 
8.6%
t 25810
 
8.0%
s 24486
 
7.6%
r 23392
 
7.2%
a 21909
 
6.8%
18965
 
5.9%
d 17027
 
5.3%
c 15052
 
4.6%
Other values (31) 86432
26.7%

Perdido
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size948.5 KiB
no
19017 
si
 
24

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters38082
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 19017
99.9%
si 24
 
0.1%

Length

2024-11-29T15:08:52.727556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-29T15:08:52.827685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 19017
99.9%
si 24
 
0.1%

Most occurring characters

ValueCountFrequency (%)
n 19017
49.9%
o 19017
49.9%
s 24
 
0.1%
i 24
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 38082
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 19017
49.9%
o 19017
49.9%
s 24
 
0.1%
i 24
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 38082
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 19017
49.9%
o 19017
49.9%
s 24
 
0.1%
i 24
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 38082
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 19017
49.9%
o 19017
49.9%
s 24
 
0.1%
i 24
 
0.1%

Saltado
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size948.5 KiB
no
19017 
si
 
24

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters38082
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 19017
99.9%
si 24
 
0.1%

Length

2024-11-29T15:08:52.923991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-29T15:08:53.014165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 19017
99.9%
si 24
 
0.1%

Most occurring characters

ValueCountFrequency (%)
n 19017
49.9%
o 19017
49.9%
s 24
 
0.1%
i 24
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 38082
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 19017
49.9%
o 19017
49.9%
s 24
 
0.1%
i 24
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 38082
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 19017
49.9%
o 19017
49.9%
s 24
 
0.1%
i 24
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 38082
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 19017
49.9%
o 19017
49.9%
s 24
 
0.1%
i 24
 
0.1%

Email Cliente
Text

Missing 

Distinct199
Distinct (%)80.2%
Missing18793
Missing (%)98.7%
Memory size604.9 KiB
2024-11-29T15:08:53.188257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length39
Median length33
Mean length22.83871
Min length8

Characters and Unicode

Total characters5664
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique183 ?
Unique (%)73.8%

Sample

1st rowSINCORREO@MNN.CL
2nd rowSINCORREO@HOTMAIL.CL
3rd rowMARITE.ARAYAC72@GMAIL.COM
4th rowDURANZAPATA.KAMILA@GMAIL.COM
5th rowJUDY@LIVE.CL
ValueCountFrequency (%)
notiene@gmail.com 9
 
3.6%
sincorreo@gmail.com 9
 
3.6%
bernarda.moya@planvital.cl 8
 
3.2%
notienecorreo@gmail.com 7
 
2.8%
yohana.hernandez@lastorres.com 5
 
2.0%
notiene@notiene.cl 4
 
1.6%
no@gmail.com 3
 
1.2%
catalinaanaismolinamolina@gmail.com 3
 
1.2%
jairo.jara@planvital.cl 3
 
1.2%
rivemepa@gmail.com 2
 
0.8%
Other values (191) 197
78.8%
2024-11-29T15:08:53.491961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 679
12.0%
O 497
 
8.8%
M 463
 
8.2%
I 449
 
7.9%
L 425
 
7.5%
C 377
 
6.7%
. 326
 
5.8%
E 325
 
5.7%
N 312
 
5.5%
R 286
 
5.0%
Other values (31) 1525
26.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5664
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 679
12.0%
O 497
 
8.8%
M 463
 
8.2%
I 449
 
7.9%
L 425
 
7.5%
C 377
 
6.7%
. 326
 
5.8%
E 325
 
5.7%
N 312
 
5.5%
R 286
 
5.0%
Other values (31) 1525
26.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5664
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 679
12.0%
O 497
 
8.8%
M 463
 
8.2%
I 449
 
7.9%
L 425
 
7.5%
C 377
 
6.7%
. 326
 
5.8%
E 325
 
5.7%
N 312
 
5.5%
R 286
 
5.0%
Other values (31) 1525
26.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5664
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 679
12.0%
O 497
 
8.8%
M 463
 
8.2%
I 449
 
7.9%
L 425
 
7.5%
C 377
 
6.7%
. 326
 
5.8%
E 325
 
5.7%
N 312
 
5.5%
R 286
 
5.0%
Other values (31) 1525
26.9%

Rating
Unsupported

Missing  Rejected  Unsupported 

Missing19041
Missing (%)100.0%
Memory size148.9 KiB

Comentario
Unsupported

Missing  Rejected  Unsupported 

Missing19041
Missing (%)100.0%
Memory size148.9 KiB

Nombre Cliente
Text

Missing 

Distinct12287
Distinct (%)85.8%
Missing4715
Missing (%)24.8%
Memory size1.2 MiB
2024-11-29T15:08:53.784398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length49
Median length44
Mean length28.550049
Min length4

Characters and Unicode

Total characters409008
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10671 ?
Unique (%)74.5%

Sample

1st rowCARLOS ALBERTO RIASCOS ALOMIA
2nd rowELVIS JHONATHAN PAREDES SIESQUEN
3rd rowJULIO CESAR CASTILLO RODRIGUEZ
4th rowPEDRO ANTONIO ANGEL BUGUENO
5th rowCAMILO ALFREDO MOLINA CARVAJAL
ValueCountFrequency (%)
del 1302
 
2.2%
carmen 1090
 
1.9%
maria 843
 
1.5%
luis 635
 
1.1%
jose 575
 
1.0%
gonzalez 566
 
1.0%
juan 542
 
0.9%
antonio 541
 
0.9%
munoz 478
 
0.8%
de 437
 
0.8%
Other values (6526) 51005
87.9%
2024-11-29T15:08:54.225878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 58161
14.2%
44214
10.8%
E 38424
9.4%
R 34952
 
8.5%
O 29450
 
7.2%
I 26346
 
6.4%
N 25966
 
6.3%
L 24323
 
5.9%
S 17841
 
4.4%
D 13284
 
3.2%
Other values (40) 96047
23.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 409008
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 58161
14.2%
44214
10.8%
E 38424
9.4%
R 34952
 
8.5%
O 29450
 
7.2%
I 26346
 
6.4%
N 25966
 
6.3%
L 24323
 
5.9%
S 17841
 
4.4%
D 13284
 
3.2%
Other values (40) 96047
23.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 409008
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 58161
14.2%
44214
10.8%
E 38424
9.4%
R 34952
 
8.5%
O 29450
 
7.2%
I 26346
 
6.4%
N 25966
 
6.3%
L 24323
 
5.9%
S 17841
 
4.4%
D 13284
 
3.2%
Other values (40) 96047
23.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 409008
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 58161
14.2%
44214
10.8%
E 38424
9.4%
R 34952
 
8.5%
O 29450
 
7.2%
I 26346
 
6.4%
N 25966
 
6.3%
L 24323
 
5.9%
S 17841
 
4.4%
D 13284
 
3.2%
Other values (40) 96047
23.5%

Formulario
Unsupported

Missing  Rejected  Unsupported 

Missing19041
Missing (%)100.0%
Memory size148.9 KiB

Segmento
Categorical

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing4965
Missing (%)26.1%
Memory size959.0 KiB
A
9127 
P
4949 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14076
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 9127
47.9%
P 4949
26.0%
(Missing) 4965
26.1%

Length

2024-11-29T15:08:54.357180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-29T15:08:54.446904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
a 9127
64.8%
p 4949
35.2%

Most occurring characters

ValueCountFrequency (%)
A 9127
64.8%
P 4949
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14076
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 9127
64.8%
P 4949
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14076
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 9127
64.8%
P 4949
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14076
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 9127
64.8%
P 4949
35.2%

ID de llamada anterior
Unsupported

Missing  Rejected  Unsupported 

Missing19041
Missing (%)100.0%
Memory size148.9 KiB

UTM
Unsupported

Missing  Rejected  Unsupported 

Missing19041
Missing (%)100.0%
Memory size148.9 KiB

ID Submotivos
Unsupported

Missing  Rejected  Unsupported 

Missing19041
Missing (%)100.0%
Memory size148.9 KiB

Submotivos de atencion
Unsupported

Missing  Rejected  Unsupported 

Missing19041
Missing (%)100.0%
Memory size148.9 KiB

Carpeta
Categorical

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing1233
Missing (%)6.5%
Memory size1.0 MiB
Afiliado
9597 
Pensionado
8211 

Length

Max length10
Median length8
Mean length8.9221698
Min length8

Characters and Unicode

Total characters158886
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAfiliado
2nd rowAfiliado
3rd rowAfiliado
4th rowAfiliado
5th rowAfiliado

Common Values

ValueCountFrequency (%)
Afiliado 9597
50.4%
Pensionado 8211
43.1%
(Missing) 1233
 
6.5%

Length

2024-11-29T15:08:54.561728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-29T15:08:54.663923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
afiliado 9597
53.9%
pensionado 8211
46.1%

Most occurring characters

ValueCountFrequency (%)
i 27405
17.2%
o 26019
16.4%
a 17808
11.2%
d 17808
11.2%
n 16422
10.3%
A 9597
 
6.0%
f 9597
 
6.0%
l 9597
 
6.0%
P 8211
 
5.2%
e 8211
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 158886
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 27405
17.2%
o 26019
16.4%
a 17808
11.2%
d 17808
11.2%
n 16422
10.3%
A 9597
 
6.0%
f 9597
 
6.0%
l 9597
 
6.0%
P 8211
 
5.2%
e 8211
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 158886
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 27405
17.2%
o 26019
16.4%
a 17808
11.2%
d 17808
11.2%
n 16422
10.3%
A 9597
 
6.0%
f 9597
 
6.0%
l 9597
 
6.0%
P 8211
 
5.2%
e 8211
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 158886
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 27405
17.2%
o 26019
16.4%
a 17808
11.2%
d 17808
11.2%
n 16422
10.3%
A 9597
 
6.0%
f 9597
 
6.0%
l 9597
 
6.0%
P 8211
 
5.2%
e 8211
 
5.2%

Categorizacion-Derivacion
Unsupported

Missing  Rejected  Unsupported 

Missing19041
Missing (%)100.0%
Memory size148.9 KiB

Modalidad de atencion
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Presencial
19041 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters190410
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPresencial
2nd rowPresencial
3rd rowPresencial
4th rowPresencial
5th rowPresencial

Common Values

ValueCountFrequency (%)
Presencial 19041
100.0%

Length

2024-11-29T15:08:54.768605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-29T15:08:54.856275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
presencial 19041
100.0%

Most occurring characters

ValueCountFrequency (%)
e 38082
20.0%
P 19041
10.0%
r 19041
10.0%
s 19041
10.0%
n 19041
10.0%
c 19041
10.0%
i 19041
10.0%
a 19041
10.0%
l 19041
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 190410
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 38082
20.0%
P 19041
10.0%
r 19041
10.0%
s 19041
10.0%
n 19041
10.0%
c 19041
10.0%
i 19041
10.0%
a 19041
10.0%
l 19041
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 190410
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 38082
20.0%
P 19041
10.0%
r 19041
10.0%
s 19041
10.0%
n 19041
10.0%
c 19041
10.0%
i 19041
10.0%
a 19041
10.0%
l 19041
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 190410
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 38082
20.0%
P 19041
10.0%
r 19041
10.0%
s 19041
10.0%
n 19041
10.0%
c 19041
10.0%
i 19041
10.0%
a 19041
10.0%
l 19041
10.0%

Tipo de cliente
Unsupported

Missing  Rejected  Unsupported 

Missing19041
Missing (%)100.0%
Memory size148.9 KiB

Pregunta Botonera
Unsupported

Missing  Rejected  Unsupported 

Missing19041
Missing (%)100.0%
Memory size148.9 KiB

Carpeta de motivos
Unsupported

Missing  Rejected  Unsupported 

Missing19041
Missing (%)100.0%
Memory size148.9 KiB

Preferencia Usuario
Unsupported

Missing  Rejected  Unsupported 

Missing19041
Missing (%)100.0%
Memory size148.9 KiB

Submotivos v2
Unsupported

Missing  Rejected  Unsupported 

Missing19041
Missing (%)100.0%
Memory size148.9 KiB

Sucursal de origen
Unsupported

Missing  Rejected  Unsupported 

Missing19041
Missing (%)100.0%
Memory size148.9 KiB

Interactions

2024-11-29T15:08:45.461511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T15:08:45.265093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T15:08:45.557258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T15:08:45.365731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-29T15:08:54.921920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
CarpetaCodigo de OficinaFilaNro. TicketOficinaPerdidoPrefijo TicketSaltadoSegmento
Carpeta1.0000.1150.8980.0660.1900.0001.0000.0000.656
Codigo de Oficina0.1151.0000.133-0.0650.9990.0150.1370.0150.110
Fila0.8980.1331.0000.0940.1570.0231.0000.0230.613
Nro. Ticket0.066-0.0650.0941.0000.2170.0000.1040.0000.004
Oficina0.1900.9990.1570.2171.0000.0350.1560.0350.166
Perdido0.0000.0150.0230.0000.0351.0000.0210.9790.000
Prefijo Ticket1.0000.1371.0000.1040.1560.0211.0000.0210.727
Saltado0.0000.0150.0230.0000.0350.9790.0211.0000.000
Segmento0.6560.1100.6130.0040.1660.0000.7270.0001.000

Missing values

2024-11-29T15:08:45.833264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-29T15:08:46.348805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-11-29T15:08:46.701606image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ID TicketID LlamadaOficinaCodigo de OficinaFilaModuloFechaPrefijo TicketNro. TicketDNI EjecutivoDNI Ejecutivo DVNombre EjecutivoDNI ClienteDNI Cliente DVHora Emision TicketHora LlamadaTiempo EsperaTiempo AtencionMotivo AtencionPerdidoSaltadoEmail ClienteRatingComentarioNombre ClienteFormularioSegmentoID de llamada anteriorUTMID SubmotivosSubmotivos de atencionCarpetaCategorizacion-DerivacionModalidad de atencionTipo de clientePregunta BotoneraCarpeta de motivosPreferencia UsuarioSubmotivos v2Sucursal de origen
0T3270200138752C2509732198327020013875217307240683370AFP PlanVital Antofagasta20Actualizacion de datos12024-11-04AD112612953-KNaNHENRIQUEZ HUERTA, VANESSA25310066-4NaN09:40:3809:41:0800:00:3000:10:30OtrosnonoNaNNaNNaNCARLOS ALBERTO RIASCOS ALOMIANaNANaNNaNNaNNaNAfiliadoNaNPresencialNaNNaNNaNNaNNaNNaN
1T3270200274435C2509732198327020027443517307334890300AFP PlanVital Antofagasta20Actualizacion de datos12024-11-04AD212612953-KNaNHENRIQUEZ HUERTA, VANESSA25328334-3NaN12:17:5412:18:0900:00:1500:06:26Actualizacion de DatosnonoNaNNaNNaNELVIS JHONATHAN PAREDES SIESQUENNaNANaNNaNNaNNaNAfiliadoNaNPresencialNaNNaNNaNNaNNaNNaN
2T3270200199076C2509732202327020019907617307247753890AFP PlanVital Antofagasta20Certificados12024-11-04AC112612953-KNaNHENRIQUEZ HUERTA, VANESSA26268695-7NaN09:48:1909:52:5500:04:3600:03:31Actualizacion de DatosnonoNaNNaNNaNJULIO CESAR CASTILLO RODRIGUEZNaNANaNNaNNaNNaNAfiliadoNaNPresencialNaNNaNNaNNaNNaNNaN
3T3270200210373C2509732202327020021037317307250432900AFP PlanVital Antofagasta20Certificados12024-11-04AC212612953-KNaNHENRIQUEZ HUERTA, VANESSA19102988-7NaN09:53:3009:57:2300:03:5300:08:57CertificadosnonoNaNNaNNaNPEDRO ANTONIO ANGEL BUGUENONaNANaNNaNNaNNaNAfiliadoNaNPresencialNaNNaNNaNNaNNaNNaN
4T3270200318250C2555532202327020031825017307254844290AFP PlanVital Antofagasta20Certificados32024-11-04AC319445974-2NaNMARJORIE NEVENKA PIZARRO OCARANZA20246505-6NaN10:01:5810:04:4400:02:4600:02:40CertificadosnonoNaNNaNNaNCAMILO ALFREDO MOLINA CARVAJALNaNANaNNaNNaNNaNAfiliadoNaNPresencialNaNNaNNaNNaNNaNNaN
5T3270200464273C2555532202327020046427317307268761580AFP PlanVital Antofagasta20Certificados32024-11-04AC419445974-2NaNMARJORIE NEVENKA PIZARRO OCARANZA24740983-1NaN10:27:4410:27:5600:00:1200:02:58CertificadosnonoNaNNaNNaNGABRIEL ZURITA RODRIGUEZNaNANaNNaNNaNNaNAfiliadoNaNPresencialNaNNaNNaNNaNNaNNaN
6T3270200587221C2555532202327020058722117307292716520AFP PlanVital Antofagasta20Certificados32024-11-04AC519445974-2NaNMARJORIE NEVENKA PIZARRO OCARANZA19966775-0NaN11:06:2711:07:5100:01:2400:03:25CertificadosnonoNaNNaNNaNVALERIA ELIZABETH MERCADO AGUIRRENaNANaNNaNNaNNaNAfiliadoNaNPresencialNaNNaNNaNNaNNaNNaN
7T3270200643964C2555532202327020064396417307302644760AFP PlanVital Antofagasta20Certificados32024-11-04AC619445974-2NaNMARJORIE NEVENKA PIZARRO OCARANZA25812443-KNaN11:24:0311:24:2400:00:2100:14:14CertificadosnonoNaNNaNNaNJOSEPH BUISSERETHNaNANaNNaNNaNNaNAfiliadoNaNPresencialNaNNaNNaNNaNNaNNaN
8T3270200784328C2545732202327020078432817307368992670AFP PlanVital Antofagasta20Certificados22024-11-04AC715769608-4NaNREINADO AGUIRRE, MARLA19444357-9NaN13:14:4413:14:5900:00:1500:19:29OtrosnonoNaNNaNNaNFRANCISCO JAVIER TAPIA MEZANaNANaNNaNNaNNaNAfiliadoNaNPresencialNaNNaNNaNNaNNaNNaN
9T3270200857982C2555532202327020085798217307420757140AFP PlanVital Antofagasta20Certificados32024-11-04AC819445974-2NaNMARJORIE NEVENKA PIZARRO OCARANZA13013165-4NaN14:40:5714:41:1500:00:1800:02:20CertificadosnonoNaNNaNNaNKATHERINE PAMELA ROJO ARAYANaNANaNNaNNaNNaNAfiliadoNaNPresencialNaNNaNNaNNaNNaNNaN
ID TicketID LlamadaOficinaCodigo de OficinaFilaModuloFechaPrefijo TicketNro. TicketDNI EjecutivoDNI Ejecutivo DVNombre EjecutivoDNI ClienteDNI Cliente DVHora Emision TicketHora LlamadaTiempo EsperaTiempo AtencionMotivo AtencionPerdidoSaltadoEmail ClienteRatingComentarioNombre ClienteFormularioSegmentoID de llamada anteriorUTMID SubmotivosSubmotivos de atencionCarpetaCategorizacion-DerivacionModalidad de atencionTipo de clientePregunta BotoneraCarpeta de motivosPreferencia UsuarioSubmotivos v2Sucursal de origen
19031T3311224590840C2554932123331122459084017328012152630AFP PlanVital Vina del Mar47Otros Pensionado32024-11-28P516887527-4NaNMAURICIO MAXIMILIANO LOBOS ROJAS13365128-4NaN10:39:5010:40:1500:00:2500:07:19CertificadosnonoNaNNaNNaNJUAN FRANCISCO PEREZ ALVAREZNaNANaNNaNNaNNaNPensionadoNaNPresencialNaNNaNNaNNaNNaNNaN
19032T3311224657236C2554832123331122465723617328046796820AFP PlanVital Vina del Mar47Otros Pensionado52024-11-28P610993573-5NaNALFARO MONDACA, CLAUDIA15083446-5NaN11:37:3711:37:5900:00:2200:13:13OtrosnonoCARANEDACISTERNAS@GMAIL.COMNaNNaNCAROLINA VIVIANA ARANEDA CISTERNASNaNNaNNaNNaNNaNNaNPensionadoNaNPresencialNaNNaNNaNNaNNaNNaN
19033T3311224781957C2554932123331122478195717328083270940AFP PlanVital Vina del Mar47Otros Pensionado32024-11-28P716887527-4NaNMAURICIO MAXIMILIANO LOBOS ROJAS19400071-5NaN12:38:0112:38:4700:00:4600:06:04OtrosnonoNaNNaNNaNCAMILA FERNANDA POLANCO DALYNaNANaNNaNNaNNaNPensionadoNaNPresencialNaNNaNNaNNaNNaNNaN
19034T3311224874340C2512032123331122487434017328083907140AFP PlanVital Vina del Mar47Otros Pensionado42024-11-28P812848773-5NaNOLIVARES LAGOS, MILENA8062217-1NaN12:39:3412:39:5000:00:1600:03:44OtrosnonoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNPensionadoNaNPresencialNaNNaNNaNNaNNaNNaN
19035T3311224144655C2554832124331122414465517327975321110AFP PlanVital Vina del Mar47Pago en exceso52024-11-28AP110993573-5NaNALFARO MONDACA, CLAUDIA10820061-8NaN09:34:0409:38:5200:04:4800:00:47Actualizacion de Datos,CertificadosnonoNaNNaNNaNLUIS ALBERTO CACHANA VIDALNaNANaNNaNNaNNaNAfiliadoNaNPresencialNaNNaNNaNNaNNaNNaN
19036T3311224230283C2554932124331122423028317328213479500AFP PlanVital Vina del Mar47Pago en exceso32024-11-28AP216887527-4NaNMAURICIO MAXIMILIANO LOBOS ROJAS12701243-1NaN16:15:3016:15:4700:00:1700:02:15OtrosnonoNaNNaNNaNGERARDO ENRIQUE MORA HERNANDEZNaNANaNNaNNaNNaNAfiliadoNaNPresencialNaNNaNNaNNaNNaNNaN
19037T3311224143947C2554932126331122414394717328199563480AFP PlanVital Vina del Mar47Solicitud tramite de pension32024-11-28PT116887527-4NaNMAURICIO MAXIMILIANO LOBOS ROJAS11621009-6NaN15:52:2315:52:3600:00:1300:07:13CertificadosnonoNaNNaNNaNJAVIER ALEJANDRO MUNOZ ALVAREZNaNANaNNaNNaNNaNPensionadoNaNPresencialNaNNaNNaNNaNNaNNaN
19038T3311204514206C2512032123331120451420617310750495730AFP PlanVital Vina del Mar47Otros Pensionado42024-11-08P512848773-5NaNOLIVARES LAGOS, MILENA7713740-8NaN11:10:1411:10:4900:00:35NaNNaNnonoNaNNaNNaNROSA ADRIANA ROJAS ALLENDENaNPNaNNaNNaNNaNPensionadoNaNPresencialNaNNaNNaNNaNNaNNaN
19039T3294202381983C2551432011329420238198317315959125190AFP PlanVital Puerto Montt94Otros32024-11-14O316262795-3NaNTOLEDO BARRIA, JESSICA CAROLINA12757126-0NaN11:51:2111:51:5200:00:31NaNNaNnonoNaNNaNNaNADAN EUGENIO GALLARDO CARCAMONaNANaNNaNNaNNaNAfiliadoNaNPresencialNaNNaNNaNNaNNaNNaN
19040T3308223599521C2553431819330822359952117327378215580AFP PlanVital Tenderini50Giro APV o Cuenta 232024-11-27AG517051427-0NaNARRIAGADA RODRIGUEZ,NAYADET10355488-8NaN17:03:1917:03:4100:00:22NaNNaNnonoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNAfiliadoNaNPresencialNaNNaNNaNNaNNaNNaN